AUTHOR=Xu Song , Zha Fang-Lin , Huang Bo-Wen , Yu Bing , Huang Hai-Bo , Zhou Ting , Mao Wen-Qi , Wu Jie-Jun , Wei Jia-Qiang , Gong Shang-Kun , Wan Tao , Duan Xin-Yu , Xiong Shang-Feng 

TITLE=Research on the state of health estimation of lithium-ion batteries for energy storage based on XGB-AKF method

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.999676

DOI=10.3389/fenrg.2022.999676

ISSN=2296-598X

ABSTRACT=With the advantages of high energy density, long cycle life and high stability, lithium-ion batteries have been used in a large number of fields such as electric vehicles and grid scale energy storage. To ensure the safe and reliable operation of battery systems, it is important to make an accurate and rapid estimation of the State of Health (SOH) of Li-ion cells. A lithium-ion cell is a complex nonlinear dynamic system. In actual working conditions, the SOH of a lithium-ion cell is difficult to measure directly, and can only be quantitatively evaluated indirectly by reflecting the external characteristic parameters of the battery aging process. The method based on a single aging feature or model is difficult to ensure the reliability. Therefore, this paper proposes a multi-feature combined SOH estimation method that combines a data-driven XGBoost and a Kalman filter (KF). Firstly, a principal component analysis (PCA) algorithm to reconstruct multiple battery aging features based on data is used, and an XGBoost online estimation model incorporating multiple features based on the reconstructed feature data is constructed in this method. Finally, the joint optimal estimation of SOH of Li-ion cells by introducing a time-domain Kalman filter based on the real-time correction of the XGBoost model is achieved in this method. The results show that the method improves the accuracy and robustness of the estimation model and achieves a high-precision joint estimation of SOH for Li-ion cells.